Now, we need to do same data preparationg before starting time series clustering. Starting from the top left graph, we have normal trend (5), cycle (105), increasing trend (205), decreasing trend (305), upward shift (405), downward shift (505). Now we try to separate ech of these six patterns. data <- read.table('C:/07 - R Website/dataset/TS/synthetic_control.txt', header = FALSE)įrom the graph above, we can see the our data have six patterns.

This dataset contains 600 examples of control charts synthetically generated by the process in Alcock and Manolopoulos (1999). We use the Synthetic Control Chart Time Series. This is a central problem in many application fields and hence time series clustering is nowadays an active research area in different disciplines including finance and economics, medicine, engineering, seismology and meteorology, among others. Time series clustering problems arise when we observe a sample of time series and we want to group them into different categories or clusters.

At different distances, different clusters will form, which can be represented using dendrogram. A cluster can be descibed by the maximum distance needed to connect parts of the cluster. Connectivity-based clustering connects objects to form clusters based on their distance. Clustering is the task of grouping s set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.